Statistical Foundations of Machine Learning by Gianluca Bontempi
Statistical Foundations of Machine Learning - Table of Contents
1. Introduction
2. Foundations of Probability
3. Parametric Estimation: The Classical Approach
4. Nonparametric Estimation and Testing
5. Statistical Supervised Learning
6. The Machine Learning Procedure
7. Linear Approaches
8. Nonlinear Approaches
9. Model Averaging Approaches
10. Feature Selection
11. Conclusions
A. Unsupervised Learning
B. Linear Algebra Notions
C. Optimisation Notions
D. Probabilistic Notions
E. Plug-in Estimators
F. Kernel Functions
G. Companion R Package
H. Companion R Shiny Dashboards
What You Will Learn in Statistical Foundations of Machine Learning
"Statistical Foundations of Machine Learning: The Handbook" by "Gianluca Bontempi" provides a rigorous introduction to the "statistical foundations" behind modern "machine learning". The book explains why algorithms work by connecting probability, estimation, and data modeling to real-world predictive tasks. It is designed for readers who want a deep understanding of the principles that guide machine learning, rather than just algorithmic procedures.
The text begins with core concepts in "probability theory" and classical statistics, building toward supervised learning methods such as regression and classification. It introduces interpretable models, including "graphical models" and Bayesian approaches, and explores challenges like high-dimensional data, feature selection, and model averaging. Each topic is presented with theoretical clarity and practical insights, helping readers understand both the assumptions and the limitations of different models.
Practical applications are integrated using "R programming" and interactive examples, enabling readers to apply statistical thinking to real datasets. Topics such as dimensionality reduction, nonlinear models, and algorithmic strategies for predictive modeling are also covered. Ideal for graduate students, researchers, and data science practitioners, this book equips readers with the statistical knowledge needed to build robust, interpretable, and reliable machine learning models in modern data-driven environments.
Book Details & Specifications
Title:
Statistical Foundations of Machine Learning by Gianluca Bontempi
Publisher:
Université Libre de Bruxelles (ULB)
Year:
2022
Pages:
364
Type:
PDF
Language:
English
ISBN-10 #:
B0D5HDT6H6
ISBN-13 #:
License:
External Educational Resource
Amazon:
Amazon
About the Author: Gianluca Bontempi
The author
Gianluca Bontempi
is a leading "machine learning" and "big data" researcher based in Brussels, Belgium. He is a Full Professor at the "Université Libre de Bruxelles (ULB)" and co-head of its Machine Learning Group. Bontempi earned his Ph.D. in computer science and specializes in "predictive modeling", "causal inference", and bioinformatics, applying these techniques to forecasting, fraud detection, and network analysis. He has published over 250 scientific papers, contributed to open-source data tools, and participated in major European research projects. His work bridges theoretical and practical aspects of "statistical learning", helping researchers and practitioners build robust, interpretable "models" for real-world applications.
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